def test_piesno():
# Values taken from hispeed.OptimalPIESNO with the test data
# in the package computed in matlab
test_piesno_data = nib.load(dipy.data.get_data("test_piesno")).get_data()
sigma = piesno(test_piesno_data, N=8, alpha=0.01, l=1, eps=1e-10,
return_mask=False)
assert_almost_equal(sigma, 0.010749458025559)
noise1 = (np.random.randn(100, 100, 100) * 50) + 10
noise2 = (np.random.randn(100, 100, 100) * 50) + 10
rician_noise = np.sqrt(noise1**2 + noise2**2)
sigma, mask = piesno(rician_noise, N=1, alpha=0.01, l=1, eps=1e-10,
return_mask=True)
# less than 3% of error?
assert_(np.abs(sigma - 50) / sigma < 0.03)
# Test using the median as the initial estimation
initial_estimation = (np.median(sigma) /
np.sqrt(2 * _inv_nchi_cdf(1, 1, 0.5)))
sigma, mask = _piesno_3D(rician_noise, N=1, alpha=0.01, l=1, eps=1e-10,
return_mask=True,
initial_estimation=initial_estimation)
assert_(np.abs(sigma - 50) / sigma < 0.03)
sigma = _piesno_3D(rician_noise, N=1, alpha=0.01, l=1, eps=1e-10,
return_mask=False,
initial_estimation=initial_estimation)
assert_(np.abs(sigma - 50) / sigma < 0.03)
sigma = _piesno_3D(np.zeros_like(rician_noise), N=1, alpha=0.01, l=1,
eps=1e-10, return_mask=False,
initial_estimation=initial_estimation)
assert_(np.all(sigma == 0))
sigma, mask = _piesno_3D(np.zeros_like(rician_noise), N=1, alpha=0.01, l=1,
eps=1e-10, return_mask=True,
initial_estimation=initial_estimation)
assert_(np.all(sigma == 0))
assert_(np.all(mask == 0))
# Check if no noise points found in array it exits
sigma = _piesno_3D(1000*np.ones_like(rician_noise), N=1, alpha=0.01, l=1,
eps=1e-10, return_mask=False, initial_estimation=10)
assert_(np.all(sigma == 10))
def test_piesno():
# Values taken from hispeed.OptimalPIESNO with the test data
# in the package computed in matlab
test_piesno_data = nib.load(dpd.get_fnames("test_piesno")).get_data()
sigma = piesno(test_piesno_data, N=8, alpha=0.01, l=1, eps=1e-10,
return_mask=False)
assert_almost_equal(sigma, 0.010749458025559)
noise1 = (np.random.randn(100, 100, 100) * 50) + 10
noise2 = (np.random.randn(100, 100, 100) * 50) + 10
rician_noise = np.sqrt(noise1**2 + noise2**2)
sigma, mask = piesno(rician_noise, N=1, alpha=0.01, l=1, eps=1e-10,
return_mask=True)
# less than 3% of error?
assert_(np.abs(sigma - 50) / sigma < 0.03)
# Test using the median as the initial estimation
initial_estimation = (np.median(sigma) /
np.sqrt(2 * _inv_nchi_cdf(1, 1, 0.5)))
sigma, mask = _piesno_3D(rician_noise, N=1, alpha=0.01, l=1, eps=1e-10,
return_mask=True,
initial_estimation=initial_estimation)
assert_(np.abs(sigma - 50) / sigma < 0.03)
sigma = _piesno_3D(rician_noise, N=1, alpha=0.01, l=1, eps=1e-10,
return_mask=False,
initial_estimation=initial_estimation)
assert_(np.abs(sigma - 50) / sigma < 0.03)
sigma = _piesno_3D(np.zeros_like(rician_noise), N=1, alpha=0.01, l=1,
eps=1e-10, return_mask=False,
initial_estimation=initial_estimation)
assert_(np.all(sigma == 0))
sigma, mask = _piesno_3D(np.zeros_like(rician_noise), N=1, alpha=0.01, l=1,
eps=1e-10, return_mask=True,
initial_estimation=initial_estimation)
assert_(np.all(sigma == 0))
assert_(np.all(mask == 0))
# Check if no noise points found in array it exits
sigma = _piesno_3D(1000*np.ones_like(rician_noise), N=1, alpha=0.01, l=1,
eps=1e-10, return_mask=False, initial_estimation=10)
assert_(np.all(sigma == 10))
def test_piesno():
# Values taken from hispeed.OptimalPIESNO with the test data
# in the package computed in matlab
test_piesno_data = nib.load(dipy.data.get_data("test_piesno")).get_data()
sigma = piesno(test_piesno_data, N=8, alpha=0.01, l=1, eps=1e-10, return_mask=False)
assert_almost_equal(sigma, 0.010749458025559)
noise1 = (np.random.randn(100, 100, 100) * 50) + 10
noise2 = (np.random.randn(100, 100, 100) * 50) + 10
rician_noise = np.sqrt(noise1**2 + noise2**2)
sigma, mask = piesno(rician_noise, N=1, alpha=0.01, l=1, eps=1e-10, return_mask=True)
# less than 3% of error?
assert_almost_equal(np.abs(sigma - 50) / sigma < 0.03, True)
# Test using the median as the initial estimation
initial_estimation = np.median(sigma) / np.sqrt(2 * _inv_nchi_cdf(1, 1, 0.5))
sigma, mask = _piesno_3D(rician_noise, N=1, alpha=0.01, l=1, eps=1e-10, return_mask=True,
initial_estimation=initial_estimation)
assert_almost_equal(np.abs(sigma - 50) / sigma < 0.03, True)